Indonesia is the largest country in Southeast Asia.Rice is the primary staple food crop with a steady increase in annual production, making Indonesia the third largest rice producer in the world. 93% of Indonesia’s total number of farmers are small family farms. Rice is the main crop grown and staple food in Southeast Asia.(Yoshida (1981)) Crop rotation is the practice of planting different crops sequentially on the same plot of land to improve soil health, optimize nutrients in the soil, and combat pest and weed pressure.(Crystal and Whittlesey (2004)) Soybean is a species of legume native to East Asia, widely grown for its edible bean which has numerous uses.(Wright et al. (2005)) Chili is a plant of tropical and subtropical regions for their fleshy fruits.(MOALF (2016))
Rice farm with crop rotation. Crop rotation for this project is chilli ana soybean. Total cost per crop are consists of labor, seeds, pesticides, fertilizer, machinery and rent land. Moreover, Revenues is yield of rice, soybean and chilli. Finally, total cost, revenues and discount rate use put to calculate to Net Present Value(NVP).
Variable for rice farm and crop rotation for small holder farmers in Indonesia have 8 mains variable and this consists of production, rice cultivation cost, soybean production, soybean cultivation cost, chilli production, chilli cultivation cost, discount rate and year of system. This project use 37 variable estimate to calculate
####Source: BPS (2018), Mucharam et al. (2020), Jagung (2017), Fao (2018), BPS (2022), Amirrullah (2019), Crystal and Whittlesey (2004), Jagung (2017), BRIN (2022), USDA (2012), Setiartiti (2021), Antriyandarti (2015), Krisdiana et al. (2021), Harsono et al. (2020), Schilling (1999), Wandschneider et al. (2019), Sundari et al. (2021)
NPV (Net Present Value): In financial terms, the NPV
the measurement of the profitability of a project or programme. This is
achieved by subtracting the current values of expenditure from the
current values of income over a period of time. Income can be referred
to as benefit and expenditure can be referred to as cost.
Discount Rate: The discount rate is the interest rate used in analysis of discounted cash flow (DCF).
Stantec (2005)
Do, Luedeling, and Whitney (2020)
crop_rotation_decision <- function(){
# Estimate the income of rice in a normal season
rice_income <- vv(rice_yield * rice_price, n=n_year, var_CV=100)
# Estimate the income of soybean in a normal season
soybean_income <- vv(soybean_yield * soybean_price, n=n_year, var_CV=100)
# Estimate the income of chili in a normal season
chili_income <- vv(chili_yield * chili_price, n=n_year, var_CV=100)
#Estimate the cost of rice farm in a normal season
rice_cost_precal <- sum(rice_land_rental_cost, rice_seeds_cost, rice_fertilizer_cost,
rice_pesticide_cost, rice_machinery_cost, rice_harvesting_cost)
rice_cost <- vv(rice_cost_precal, n=n_year, var_CV=100)
#Estimate the cost of soybean farm in a normal season
soybean_cost_precal <- sum(soybean_land_rental_cost, soybean_seeds_cost, soybean_fertilizer_cost,
soybean_pesticide_cost, soybean_machinery_cost, soybean_harvesting_cost)
soybean_cost <- vv(soybean_cost_precal, n=n_year, var_CV=100)
#Estimate the cost in a normal season
chili_cost_precal <- sum(chili_land_rental_cost, chili_seeds_cost, chili_fertilizer_cost,
chili_pesticide_cost, chili_machinery_cost, chili_harvesting_cost)
chili_cost <- vv(chili_cost_precal, n=n_year, var_CV=100)
# Estimate the profit
rice_profit <- vv(rice_income - rice_cost, n=n_year, var_CV=100)
soybean_profit <- vv(soybean_income - soybean_cost, n=n_year, var_CV=100)
chili_profit <- vv(chili_income - chili_cost, n=n_year, var_CV=100)
# Final result
#assuming rice cultivation is 3 times per year
rice_cultivation_result = vv(rice_profit*3, n=n_year, var_CV=100)
#crop rotation decision scenario
#if crop rotation of 3 crops is done in one year
crop_rotation_result = vv(rice_profit + soybean_profit + chili_profit, n=n_year, var_CV=100)
#if crop rotation of rice and soybean is done in one year (rice-soybean-rice)
rice_soybean_result = vv((rice_profit*2) + soybean_profit, n=n_year, var_CV=100)
#if crop rotation of rice and chili is done in one year (rice-chili)
rice_chili_result = vv(rice_profit + chili_profit, n=n_year, var_CV=100)
# NPV
NPV_rice <- discount(rice_cultivation_result, discount_rate, calculate_NPV = TRUE)
NPV_crop_rotation <- discount(crop_rotation_result, discount_rate, calculate_NPV = TRUE)
NPV_rice_soybean <- discount(rice_soybean_result, discount_rate, calculate_NPV = TRUE)
NPV_rice_chili <- discount(rice_chili_result, discount_rate, calculate_NPV = TRUE)
# Cashflow
cashflow_crop_rotation <- crop_rotation_result - rice_cultivation_result
cashflow_rice_soybean <- rice_soybean_result - rice_cultivation_result
cashflow_rice_chili <- rice_chili_result - rice_cultivation_result
# Generate the list of outputs from the Monte Carlo simulation
return(list(Rice_NPV = NPV_rice,
crop_rotation_NPV = NPV_crop_rotation,
rice_soybean_NPV = NPV_rice_soybean,
rice_chili_NPV= NPV_rice_chili,
NPV_decision_crop_rotation = NPV_crop_rotation - NPV_rice,
NPV_decision_rice_soybean = NPV_rice_soybean - NPV_rice,
NPV_decision_rice_chili = NPV_rice_chili - NPV_rice,
cashflow_crop_rotation = cashflow_crop_rotation,
cashflow_rice_soybean = cashflow_rice_soybean,
cashflow_rice_chili = cashflow_rice_chili
))
}
make_variables<-function(est,n=1)
{ x<-random(rho=est, n=n)
for(i in colnames(x)) assign(i, as.numeric(x[1,i]),envir=.GlobalEnv)}
make_variables(read.csv("new_variable_estimates.csv"))
## Warning in assign(i, as.numeric(x[1, i]), envir = .GlobalEnv): NAs introduced
## by coercion
# Run the Monte Carlo simulation using the model function
input_estimates <- read.csv("new_variable_estimates.csv", sep=";")
crop_rotation_mc_simulation <- mcSimulation(estimate = as.estimate(input_estimates),
model_function = crop_rotation_decision,
numberOfModelRuns = 1000,
functionSyntax = "plainNames")
# Run the Monte Carlo simulation using the model function
input_estimates <- read.csv("new_variable_estimates.csv", sep=";")
crop_rotation_mc_simulation <- mcSimulation(estimate = as.estimate(input_estimates),
model_function = crop_rotation_decision,
numberOfModelRuns = 1000,
functionSyntax = "plainNames")
#if rice with soybean and chili (rice-soybean-chili)
decisionSupport::plot_distributions(mcSimulation_object = crop_rotation_mc_simulation,
vars = c("NPV_decision_crop_rotation", "Rice_NPV"),
method = 'smooth_simple_overlay')
decisionSupport::plot_distributions(mcSimulation_object = crop_rotation_mc_simulation,
vars = "NPV_decision_crop_rotation",
method = 'boxplot')
#if rice with soybean (rice-soybean-rice)
decisionSupport::plot_distributions(mcSimulation_object = crop_rotation_mc_simulation,
vars = c("NPV_decision_rice_soybean","Rice_NPV"),
method = 'smooth_simple_overlay')
decisionSupport::plot_distributions(mcSimulation_object = crop_rotation_mc_simulation,
vars = "NPV_decision_rice_chili",
method = 'boxplot')
#if rice with chili (rice-chili)
decisionSupport::plot_distributions(mcSimulation_object = crop_rotation_mc_simulation,
vars = c("NPV_decision_rice_chili","Rice_NPV"),
method = 'smooth_simple_overlay')
decisionSupport::plot_distributions(mcSimulation_object = crop_rotation_mc_simulation,
vars = "NPV_decision_rice_chili",
method = 'boxplot')
plot_cashflow(mcSimulation_object = crop_rotation_mc_simulation, cashflow_var_name = "cashflow_crop_rotation")
plot_cashflow(mcSimulation_object = crop_rotation_mc_simulation, cashflow_var_name = "cashflow_rice_soybean")
plot_cashflow(mcSimulation_object = crop_rotation_mc_simulation, cashflow_var_name = "cashflow_rice_chili")
mcSimulation_table <- data.frame(crop_rotation_mc_simulation$x, crop_rotation_mc_simulation$y[1:7])
evpi_crop_rotation <- multi_EVPI(mc = mcSimulation_table, first_out_var = "crop_rotation_NPV")
## [1] "Processing 6 output variables. This can take some time."
## [1] "Output variable 1 (crop_rotation_NPV) completed."
## [1] "Output variable 2 (rice_soybean_NPV) completed."
## [1] "Output variable 3 (rice_chili_NPV) completed."
## [1] "Output variable 4 (NPV_decision_crop_rotation) completed."
## [1] "Output variable 5 (NPV_decision_rice_soybean) completed."
## [1] "Output variable 6 (NPV_decision_rice_chili) completed."
plot_evpi(evpi_crop_rotation, decision_vars = "NPV_decision_crop_rotation")
evpi_rice_soybean <- multi_EVPI(mc = mcSimulation_table, first_out_var = "rice_soybean_NPV")
## [1] "Processing 5 output variables. This can take some time."
## [1] "Output variable 1 (rice_soybean_NPV) completed."
## [1] "Output variable 2 (rice_chili_NPV) completed."
## [1] "Output variable 3 (NPV_decision_crop_rotation) completed."
## [1] "Output variable 4 (NPV_decision_rice_soybean) completed."
## [1] "Output variable 5 (NPV_decision_rice_chili) completed."
plot_evpi(evpi_rice_soybean, decision_vars = "NPV_decision_rice_soybean")
evpi_rice_chili <- multi_EVPI(mc = mcSimulation_table, first_out_var = "rice_chili_NPV")
## [1] "Processing 4 output variables. This can take some time."
## [1] "Output variable 1 (rice_chili_NPV) completed."
## [1] "Output variable 2 (NPV_decision_crop_rotation) completed."
## [1] "Output variable 3 (NPV_decision_rice_soybean) completed."
## [1] "Output variable 4 (NPV_decision_rice_chili) completed."
plot_evpi(evpi_rice_chili, decision_vars = "NPV_decision_rice_chili")
pls_result_crop_rotation <- plsr.mcSimulation(object = crop_rotation_mc_simulation,
resultName = names(crop_rotation_mc_simulation$y)[5], ncomp = 1)
plot_pls(pls_result_crop_rotation, threshold = 0)
pls_result_rice_soybean <- plsr.mcSimulation(object = crop_rotation_mc_simulation,
resultName = names(crop_rotation_mc_simulation$y)[6], ncomp = 1)
plot_pls(pls_result_rice_soybean, threshold = 0)
pls_result_rice_chili <- plsr.mcSimulation(object = crop_rotation_mc_simulation,
resultName = names(crop_rotation_mc_simulation$y)[7], ncomp = 1)
plot_pls(pls_result_rice_chili, threshold = 0)
compound_figure(mcSimulation_object = crop_rotation_mc_simulation,
input_table = input_estimates, plsrResults = pls_result_crop_rotation,
EVPIresults = evpi_crop_rotation, decision_var_name = "NPV_decision_crop_rotation",
cashflow_var_name = "cashflow_crop_rotation",
base_size = 7)
compound_figure(mcSimulation_object = crop_rotation_mc_simulation,
input_table = input_estimates, plsrResults = pls_result_rice_soybean,
EVPIresults = evpi_rice_soybean, decision_var_name = "NPV_decision_rice_soybean",
cashflow_var_name = "cashflow_rice_soybean",
base_size = 7)
compound_figure(mcSimulation_object = crop_rotation_mc_simulation,
input_table = input_estimates, plsrResults = pls_result_rice_chili,
EVPIresults = evpi_rice_chili, decision_var_name = "NPV_decision_rice_chili",
cashflow_var_name = "cashflow_rice_chili",
base_size = 7)
We recommend Indonesian smallholder farmers to implement crop rotation either for three crops (rice, soybean, and chili) or two crops (rice and chili) as it seems more profitable than growing rice only all year around. However, we would not recommend to implement crop rotation between rice and soybean as it seems not so profitable.
Rice farming with crop rotation of soybean and chili can be implemented by Indonesian smallholder farmers to get higher income. However, not all the crops are profitable to be rotated with rice. There are more uncertainties in crop rotation of rice and soybean compared to other scenarios. Thus, further data and research still needed.